Glossary of Technical Terms Generalized Lotka-Volterra Equation: Biomass (M): the total mass of living organisms in a certain ecosystem divided by the total number of species, or the average population size Biodiversity (D): the diversity of plant and animal life in a specific habitat Project Description: In this project we seek to: model coexistence and chaos in ecosystems, show the effects of adaptation and mutation on population dynamics, reproduce the results of Sprott, et al [1], who employed the Lotka- Volterra Equations to model population dynamics, and to observe the effect that changes in adaptation have on the model. Through the use of a computer simulation we hope to accurately model large numbers of species in an ecosystem. Scientific Challenges To accurately capture the chaos amongst populations through a numerical simulation Potential Applications Survival and extinction in the wild Financial markets Traffic flow Competing Species Team Members: David De Cesari (Engineering Mathematics), Jennifer Kanemaru (Mathematics), Daniel Weiss (Aerospace Engineering, Mathematics), Carolyn Wise (Mathematics) Biomass and Biodiversity graphs with initial conditions of clamp size = and mutation at every 20 time steps with a time step size of Methodology To achieve our goals, we: 1.simulated population dynamics using the generalized Lotka-Volterra equation, which includes a competition matrix that dictates how the various species interact, 2.employed a classical Runge Kutta method in MATLAB, 3.simulated adaptation by altering the interaction matrix every twenty time steps. We increased the off-diagonal elements of the matrix by a small value to model increasing environmental stress, as well as enhanced competition, 4.prevented extinction by clamping a failing species at a small value, 5.investigated the role of adaptation by changing how often we altered the interaction matrix and changing the value at which we clamped a dying species. 6.In order to obtain easily understandable results, we generated the measures Biomass and Biodiversity. Biomass with mutation at every 5 and 40 time steps, respectively Biodiversity with mutation at every 5 and 40 time steps, respectively Results 1.We were able to approximately replicate the results of Sprott [1] in terms of biomass and biodiversity. 2.For the modifications of mutation, noticeable visual differences were seen between the different mutation time steps. Higher mutation rates produced a lower biomass and a lower biodiversity. Fluctuations decrease as adaptation is simulated less frequently (as seen above). This mutation is due to environmental stress [1], and therefore with higher mutation rates, each species is less likely to flourish. 3.With our initial clamp size of there was a sharp decline in biodiversity over the initial few time steps. When the clamp size was changed by a factor of 100 in either direction, there was negligible change; this was most likely due to the fact that these values are still extremely small in comparison to the population levels of the various species. References 1.Sprott, J.C., J.C. Vano, J.C. Wildenberg, M.B. Anderson, and J.K. Noel. "Coexistence and chaos in complex ecologies." Physics Letters A 335. (2005): Web. 23 Feb Fox, J. W., and D. A. Vasseur Character convergence under competition for nutritionally-essential resources. American Naturalist 172: Acknowledgments This project was mentored by Sarah Mann, whose help is acknowledged with great appreciation, as well as Dr. Ildar Gabitov. Biodiversity graphs with mutation at every 20 time steps with a time step size of and clamp sizes of and 10 -2, respectively